toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
   print
  Records Links (down)
Author Straka III, W.; Kondragunta, S.; Wei, Z.; Zhang, H.; Miller, S.D.; Watts, A. url  doi
openurl 
  Title Examining the Economic and Environmental Impacts of COVID-19 Using Earth Observation Data Type Journal Article
  Year 2021 Publication Remote Sensing Abbreviated Journal Remote Sensing  
  Volume 13 Issue 1 Pages 5  
  Keywords Remote Sensing; COVID-19  
  Abstract The COVID-19 pandemic has infected almost 73 million people and is responsible for over 1.63 million fatalities worldwide since early December 2019, when it was first reported in Wuhan, China. In the early stages of the pandemic, social distancing measures, such as lockdown restrictions, were applied in a non-uniform way across the world to reduce the spread of the virus. While such restrictions contributed to flattening the curve in places like Italy, Germany, and South Korea, it plunged the economy in the United States to a level of recession not seen since WWII, while also improving air quality due to the reduced mobility. Using daily Earth observation data (Day/Night Band (DNB) from the National Oceanic and Atmospheric Administration Suomi-NPP and NO2 measurements from the TROPOspheric Monitoring Instrument TROPOMI) along with monthly averaged cell phone derived mobility data, we examined the economic and environmental impacts of lockdowns in Los Angeles, California; Chicago, Illinois; Washington DC from February to April 2020—encompassing the most profound shutdown measures taken in the U.S. The preliminary analysis revealed that the reduction in mobility involved two major observable impacts: (i) improved air quality (a reduction in NO2 and PM2.5 concentration), but (ii) reduced economic activity (a decrease in energy consumption as measured by the radiance from the DNB data) that impacted on gross domestic product, poverty levels, and the unemployment rate. With the continuing rise of COVID-19 cases and declining economic conditions, such knowledge can be combined with unemployment and demographic data to develop policies and strategies for the safe reopening of the economy while preserving our environment and protecting vulnerable populations susceptible to COVID-19 infection.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2072-4292 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 3250  
Permanent link to this record
 

 
Author Yue, Y.; Tian, L.; Yue, Q.; Wang, Z. url  doi
openurl 
  Title Spatiotemporal Variations in Energy Consumption and Their Influencing Factors in China Based on the Integration of the DMSP-OLS and NPP-VIIRS Nighttime Light Datasets Type Journal Article
  Year 2020 Publication Remote Sensing Abbreviated Journal Remote Sensing  
  Volume 12 Issue 7 Pages 1151  
  Keywords Remote Sensing  
  Abstract With the speedy growth of economic development, the imbalance of energy supply and demand pose a critical challenge for the energy security of our country. Meanwhile, the increasing and excessive energy consumption lead to the greenhouse effect and atmospheric pollution, greatly threatening the survival and development of human beings. This study integrated two nighttime light remote sensing datasets, namely Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data and Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) data, to extend the temporal coverage of the study. Then, the distributions of China’s energy consumption from 1995 to 2016 at a 1-km resolution were estimated using different models and the spatiotemporal variations of energy consumption were explored on the basis of the best estimated results. Next, the factors influencing China’s energy intensity on the provincial level were investigated based on the spatial econometric model. The results show that: (1) The integrated nighttime light datasets can be successfully applied to estimate the dynamic changes of energy consumption. Moreover, the panel data model established in our research performed better than the quadratic polynomial model. (2) During the observation period, the energy consumption in China significantly increased, especially in the Yangtze River Delta, the Pearl River Delta, the Beijing–Tianjin–Hebei region, eastern coastal cities, and provincial capitals. (3) Different from the random spatial distribution pattern of energy consumption on the provincial level, the spatial distribution of energy consumption on the prefectural level has significant clusters, and its spatial agglomeration was strengthened year by year during the research period. (4) The spatial Durbin model (SDM) with a spatial fixed effect has been proved to be more suitable to explore the impact mechanism of China’s energy consumption. Among the four socio-economic factors, industrial structure has the greatest impact on the provincial energy intensity in China. Moreover, the changes in industrial structure and foreign direct investment (FDI) can not only influence the local energy intensity but also affect the energy intensity of the neighboring provinces.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2072-4292 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2922  
Permanent link to this record
 

 
Author Ma, J.; Guo, J.; Ahmad, S.; Li, Z.; Hong, J. url  doi
openurl 
  Title Constructing a New Inter-Calibration Method for DMSP-OLS and NPP-VIIRS Nighttime Light Type Journal Article
  Year 2020 Publication Remote Sensing Abbreviated Journal Remote Sensing  
  Volume 12 Issue 6 Pages 937  
  Keywords Remote Sensing  
  Abstract The anthropogenic nighttime light (NTL) data that are acquired by satellites can characterize the intensity of human activities on the ground. It has been widely used in urban development assessment, socioeconomic estimate, and other applications. However, currently, the two main sensors, Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Satellite’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), provide inconsistent data. Hence, the application of NTL for long-term analysis is hampered. This study constructed a new inter-calibration method for DMSP-OLS and NPP-VIIRS nighttime light to solve this problem. First, NTL data were processed to obtain vicarious site across China. By comparing different candidate models, it is discovered the Biphasic Dose Response (BiDoseResp) model, which is a weighted combination of sigmoid functions, can best perform the regression between DMSP-OLS and logarithmically transformed NPP-VIIRS. The coefficient of determination of BiDoseResp model reaches 0.967. It’s residual sum of squares is 6.136×105 , which is less than 6.199×105 of Logistic function. After obtaining the BiDoseResp-calibrated VIIRS (BDRVIIRS), we smoothed it by a filter with optimal parameters to maximize the consistency. The result shows that the consistency of NTL data is greatly enhanced after calibration. In 2013, the correlation coefficient between DMSP-OLS and original NPP-VIIRS data in the China region is only 0.621, while that reaches to 0.949 after calibration. Finally, a consistent NTL dataset of China from 1992 to 2018 was produced. When compared with the existing methods, our method is applicable to the full dynamic range of DMSP-OLS. Besides, it is more suitable for country or larger scale areas. It is expected that this method can greatly facilitate the development of research that is based on the historical NTL archive.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2072-4292 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2860  
Permanent link to this record
 

 
Author Danesh Yazdi, M.; Kuang, Z.; Dimakopoulou, K.; Barratt, B.; Suel, E.; Amini, H.; Lyapustin, A.; Katsouyanni, K.; Schwartz, J. url  doi
openurl 
  Title Predicting Fine Particulate Matter (PM2.5) in the Greater London Area: An Ensemble Approach using Machine Learning Methods Type Journal Article
  Year 2020 Publication Remote Sensing Abbreviated Journal Remote Sensing  
  Volume 12 Issue 6 Pages 914  
  Keywords Remote Sensing  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2072-4292 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2856  
Permanent link to this record
 

 
Author Duan, X.; Hu, Q.; Zhao, P.; Wang, S.; Ai, M. url  doi
openurl 
  Title An Approach of Identifying and Extracting Urban Commercial Areas Using the Nighttime Lights Satellite Imagery Type Journal Article
  Year 2020 Publication Remote Sensing Abbreviated Journal Remote Sensing  
  Volume 12 Issue 6 Pages 1029  
  Keywords Remote Sensing  
  Abstract Urban commercial areas can reflect the spatial distribution of business activities. However, the scope of urban commercial areas cannot be easily detected by traditional methods because of difficulties in data collection. Considering the positive correlation between business scale and nighttime lighting, this paper proposes a method of urban commercial areas detection based on nighttime lights satellite imagery. First, an imagery preprocess model is proposed to correct imageries and improve efficiency of cluster analysis. Then, an exploratory spatial data analysis and hotspots clustering method is employed to detect commercial areas by geographic distribution metric with urban commercial hotspots. Furthermore, four imageries of Wuhan City and Shenyang City are selected as an example for urban commercial areas detection experiments. Finally, a comparison is made to find out the time and space factors that affect the detection results of the commercial areas. By comparing the results with the existing map data, we are convinced that the nighttime lights satellite imagery can effectively detect the urban commercial areas. The time of image acquisition and the vegetation coverage in the area are two important factors affecting the detection effect. Harsh weather conditions and high vegetation coverage are conducive to the effective implementation of this method. This approach can be integrated with traditional methods to form a fast commercial areas detection model, which can then play a role in large-scale socio-economic surveys and dynamic detection of commercial areas evolution. Hence, a conclusion can be reached that this study provides a new method for the perception of urban socio-economic activities.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2072-4292 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2870  
Permanent link to this record
Select All    Deselect All
 |   | 
Details
   print

Save Citations:
Export Records: